{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWK755-FnxSb"
      },
      "source": [
        "<font face=\"XB Zar\" size=5><div dir=rtl align=center>\n",
        "<font face=\"IranNastaliq\" size=5>\n",
        "به نام خدا\n",
        "</font>\n",
        "<br>\n",
        "<font size=3>\n",
        "دانشگاه صنعتی شریف - دانشکده مهندسی کامپیوتر\n",
        "</font>\n",
        "<br>\n",
        "<font color=blue size=5>\n",
        "مقدمه‌ای بر یادگیری ماشین\n",
        "</font>\n",
        "\n",
        "<hr/>\n",
        "<font color=red size=6>\n",
        "فصل دوم:مرور روش‌های کلاسیک یادگیری ماشین \n",
        "<br>\n",
        "مبحث:خوشه بندی\n",
        "</font>\n",
        "<br>\n",
        "نویسنده:‌ حدیث احمدیان\n",
        "<hr>\n",
        "    <div align=\"right\">\n",
        "  <font color=\"red\" size=5>فهرست مطالب</font>\n",
        "  <br>\n",
        "  <font size=4>\n",
        "\t<ul>\n",
        "        <li>\n",
        "        <a href=\"#1\">\n",
        "        1- مقدمه\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        <ul>\n",
        "    <li>\n",
        "        <a href=\"#1-1\">\n",
        "        1-1. یادگیری بدون ناظر\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "    <li>\n",
        "        <a href=\"#1-2\">\n",
        "        1-2. خوشه‌بندی\n",
        "        </a>\n",
        "        </li>\n",
        "        <ul>\n",
        "        <li>\n",
        "        <br>\n",
        "        <a href=\"#1-2-1\">\n",
        "        1-2-1. انواع خوشه‌بندی\n",
        "        </a>\n",
        "        </li>\n",
        "        <li>\n",
        "        <a href=\"#1-2-2\">\n",
        "        <br>\n",
        "        1-2-2. تعریف خوشه\n",
        "        </a>\n",
        "         </li>\n",
        "    </ul>\n",
        "     </ul>\n",
        "    <br>\n",
        "        <li>\n",
        "        <a href=\"#2\">\n",
        "        2- k_means\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        <ul>\n",
        "    <li>\n",
        "        <a href=\"#2-1\">\n",
        "        2-1. معرفی الگوریتم\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "    <li>\n",
        "        <a href=\"#2-2\">\n",
        "       2-2. پیاده سازی k-means\n",
        "        </a>\n",
        "        </li>\n",
        "     <br>\n",
        "    <li>\n",
        "        <a href=\"#2-3\">\n",
        "       2-3. مقداردهی اولیه مراکز خوشه ها\n",
        "        </a>\n",
        "        </li>\n",
        "    <br>\n",
        "    <li>\n",
        "        <a href=\"#2-4\">\n",
        "       2-4. بهبودهای k-means\n",
        "        </a>\n",
        "        </li>\n",
        "        <br>\n",
        "        <ul>\n",
        "    <li>\n",
        "        <a href=\"#2-4-1\">\n",
        "        2-4-1.K-means++\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     <li>\n",
        "        <a href=\"#2-4-2\">\n",
        "        2-4-2.K-means تسریع شده\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     <li>\n",
        "        <a href=\"#2-4-3\">\n",
        "        2-4-3.mini-batch K-means\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "    </ul>\n",
        "        <li>\n",
        "        <a href=\"#2-5\">\n",
        "        2-5.انتخاب تعداد خوشه‌ها\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        <ul>\n",
        "     <li>\n",
        "        <a href=\"#2-5-1\">\n",
        "        2-5-1.استفاده از inertia\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     <li>\n",
        "        <a href=\"#2-5-2\">\n",
        "        2-5-2.silhouette score\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     <li>\n",
        "        <a href=\"#2-5-3\">\n",
        "        2-5-3.DB index\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        </ul>\n",
        "        <li>\n",
        "        <a href=\"#2-6\">\n",
        "        2-6.محدودیت‌های k-means\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     </ul>\n",
        "        <li>\n",
        "        <a href=\"#3\">\n",
        "        3.کاربردهای خوشه بندی\n",
        "        </a>\n",
        "\t</li> \n",
        "          <br>\n",
        "    <ul>\n",
        "     <li>\n",
        "        <a href=\"#3-1\">\n",
        "        3-1. Image Segmentation\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "     <li>\n",
        "        <a href=\"#3*2\">\n",
        "        3-2. پیش پردازش داده‌ها\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        <li>\n",
        "        <a href=\"#3-3\">\n",
        "        3-3. Semi-Supervised learning\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "        <li>\n",
        "        <a href=\"#3-4\">\n",
        "        3-4. یادگیری فعال (active learning)\n",
        "        </a>\n",
        "\t</li>\n",
        "    <br>\n",
        "       \n",
        "            \n",
        "   <br>\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffDQcr6isJk4"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"1\">\n",
        "<font color=\"red\" size=5>**1.مقدمه**</font>\n",
        "<br>\n",
        "    <br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"1-1\">\n",
        "<font color=\"red\" size=4>**1-1. یادگیری بدون ناظر (Unsupervised learning)**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O70q6bhSv_lt"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "در دنیای امروزی اکثر کاربردهای یادگیری ماشین  مبتنی بر یاگیری با ناظر (supervised learning) هستند. این بدین معنی است که برای این کاربردها نیاز به داده های برچسب‌دار داریم یعنی برای آموزش مدل خود علاوه بر ویژگی ها باید برچسب داده های مورد نظر را نیز داشته باشیم حال آنکه داده‌های برچسب دار تنها بخش بسیار کوچکی از تمام داده‌های موجود هستند.\n",
        "<br>\n",
        "<br>\n",
        "اجازه دهید با یک مثال بیشتر به این موضوع بپردازیم. فرض کنید در یک کارخانه که کالایی را تولید می‌کند وظیفه داریم کالای سالم از کالای خراب را تشخیص دهیم و برای انجام این کار می خواهیم یک مدل آموزش دهیم. ساخت داده بدون برچسب برای این کار بسیار راحت است. کافیست یک دوربین در اختیار داشته باشیم که از تمام کالاهای تولید شده در خط تولید عکسبرداری کند اما تنها با داشتن این عکس ها نمیتوانیم به صورت supervised عمل کنیم، زیرا نمی‌دانیم که هرعکس متعلق به کالای سالم است یا یک کالای خراب. به بیان دیگر برچسب متعلق به هر داده را در اختیار نداریم.برای برچسب گذاری داده ها نیاز به یک اپراتور انسانی داریم که تک تک عکس ها را بررسی کند و مشخص کند که آن کالا سالم بوده است یا خیر. می دانیم که این کار بسیار پر هزینه خواهد بود، به همین دلیل اغلب مجبور خواهیم بود تنها بخش کوچکی از داده‌های موجود را به طور تصادفی انتخاب کنیم و تنها آن بخش کوچک را برچسب گذاری کنیم.\n",
        "<br>\n",
        "<br>\n",
        " اولین مشکل این است که به دلیل کوچک بودن مجموعه داده های برچسب دار، نمی توان مدل  توانمندی را آموزش داد. علاوه بر این اگر بخش هایی از کالاهای تولیدی توسط کارخانه تغییر کند، دیگر مدل ما قابل استفاده نخواهد بود و باید از اول عکس های کالاهای جدید برچسب گذاری شود و مدل با داده های جدید آموزش ببیند.\n",
        "<br>\n",
        "<br>\n",
        " یک مثال جالب برای مقایسه ی میزان داده های برچسب دار و بدون برچسب موجود، مثال کیک و گیلاس است. اگر داده های بدون برچسب موجود را به اندازه یک کیک در نظر بگیریم، داده های برچسب دار تنها به اندازه گیلاس کوچکی روی کیک است!\n",
        " پس مشخص است که اگر ما رویکردی در اختیار داشتیم که بدون نیاز به برچسب داده ها بتواند یک مدل را آموزش دهد مزیت بزرگی برای ما خواهد بود  به این دلیل که تعداد داده های بدون برچسب در دسترس در حال حاضر بسیار بیشتر از داده های برچسب دار است و برچسب گذاری یک پروسه‌ی پرهزینه است.\n",
        "<br>\n",
        "<br>\n",
        "به رویکرد هایی که بدون نیاز به برچسب میتوانند از داده ها استفاده کنند روشهای بدون ناظر یا unsupervised گفته می شود.\n",
        "<br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mmWrqsqgjS9"
      },
      "source": [
        "![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQPSJiCfgeheZmrwGW_f06cRHOI-7LacSUvBzshOguL1KDPdxbZBnuTEGwSBwEHjcunTdI&usqp=CAU)\n",
        "\n",
        "\n",
        "<a href=\"https://www.123rf.com/photo_124142961_womans-hand-puts-a-cherry-on-top-of-a-cake-on-the-white-blue-background-square-vector-illustration.html\">pic source</a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kh34Le73gjS-"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"1-2\">\n",
        "<font color=\"red\" size=4>**1-2. خوشه‌بندی (Clustring)**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7EWjlqIgjS-"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "خوشه بندی از رویکردهای Unsupervised  است که هدف اصلی در آن، گروه بندی نمونه هاست به صورتی که نمونه های مشابه در یک گروه قرار بگیرد. خوشه بندی در کاربرد های زیادی استفاده می‌شود از این کاربردها می توان به  سیستم های recomender، موتورهای جستجو، سگمنتیشن تصاویر، یادگیریsemi-supervised،  کاهش بعد و... اشاره کرد.\n",
        "<br>\n",
        "<br>\n",
        "    مثال زیر را در نظر بگیرید، در نمودار سمت راست داده‌ها برچسب دارند و یک رویکرد با ناظر می‌تواند از این داده‌ها استفاده کند (مانند classification) اما همان نمونه‌ها در سمت چپ را بدون برچسب‌هایشان مشاهده می‌کنید و هرچند این داده ها برچسب ندارند، اما باز هم جدا بودن بخشی از داده‌ها نسبت به سایر داده‌ها واضح است. این نشان می‌دهد بدون داشتن برچسب داده‌ها، ویژگی‌ها بدون داشتن برچسب ها می‌توانند اطلاعات کافی برای نسبت دادن هر نمونه به یک گروه را در اختیار ما قرار دهند که این همان رویکرد بدون ناظر است(مانند خوشه‌بندی).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sULNRWQ0gjS_"
      },
      "source": [
        "![](https://freddyox.github.io/images/kmeans/4Means_example_success.png )\n",
        "\n",
        "<a href=\"https://freddyox.github.io/blog/Kmeans/\">pic source</a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9rz5f-GgjTA"
      },
      "source": [
        "\n",
        "\n",
        "   \n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "<font color=\"red\" size=4><div id=\"1-2-1\">1-2-1. انواع خوشه بندی :</font>\n",
        "<br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "**الف ) دسته بندی بر اساس نرم یا سخت بودن**\n",
        "<br>\n",
        "**سخت (hard) :** هر نمونه دقیقا به یک خوشه نسبت داده میشود. در واقع خروجی این است که هر نمونه دقیقا متعلق به کدام خوشه است.\n",
        "<br>\n",
        "**نرم (soft) :** هر نمونه  با احتمالی بین 0و 1 به چندین خوشه نسبت داده میشود. در واقع خورجی این است که احتمال تعلق هر نمونه به هر کدام از خوشه ها چقدر است.\n",
        "<br>\n",
        "<br>\n",
        "**الف ) دسته بندی بر اساس patitional  یا hierachial بودن**\n",
        "<br>\n",
        "**patitional :** تمام خوشه ها در سطح یکسانی قرار دارند.\n",
        "<br>\n",
        "**سلسه مراتبی (hierachial) :** وقتی به شکل سلسه مراتبی با ادغام خوشه های کوچکتر به خوشه های بزرگتر میرسیم. ئرواقع هر خوشه خود میتواند زیر مجموعه ی یک خوشه ی سطح بالاتر باشد و چند خوشه ی کوچکتر را در بر داشته باشد.این یعنی خوشه ها سطح دارد و همه در سطح یکسانی نیستند.\n",
        "<br>\n",
        " اگر در ابتدا هر نمونه را یک خوشه در نظر بگیریم و سپس خوشه های نزئیک را باهم ادغام کنیم تا نهایتا به یک خوشه برسیم این روش خوشه بندی سلسه مراتبی به صورت تجمعی خواهد بود. رویکرد متفاوت میتواند این باشد که تمام داده ها یک خوشه در نظر گرفته شود و سپس خوشه های بزرگتر با تقسیم سلسه مراتبی به خوشه های کوچکتر تبدیل شوند.\n",
        "<br>\n",
        "    <br>\n",
        "    <br>\n",
        "    <br>\n",
        "<font color=\"red\" size=4><div dir=rtl id=\"1-2-2\">1-2-2. تعریف خوشه :</font>\n",
        "<br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "حال که با مفهوم و انواع خوشه‌بندی آشنا شدیم شاید جالب باشد درمورد مفهوم خود خوشه هم به طور دقیق‌تر صحبت کنیم. هیچ تعریف جهانی ای برای خوشه وجود ندارد و تعریف خوشه کاملاً به کاربرد ما بستگی دارد. بعضی از الگوریتم‌ها به دنبال داده‌هایی هستند که از یک نقطه‌ی مشخص به نام مرکز خوشه، کمترین فاصله را داشته باشند. برخی به دنبال نواحی از از نمونه‌ها هستند که چگالی بیشتری دارند ودر آن ها خوشه‌ها هر شکلی می‌توانند داشته باشند. برخی دیگر از الگوریتم‌ها نیز به طور سلسه مراتبی خوشه‌بندی می‌کنند ودرواقع به دنبال خوشه ای از خوشه‌ها هستند.\n",
        "<br>\n",
        "    <br>\n",
        "    <br>\n",
        "    <br>\n",
        "    در ادامه یکی از مهم‌ترین الگوریتم‌های خوشه‌بندی به نام k-means را مورد بررسی قرار می‌دهیم و سپس جزییات و کاربردهای بیشتر از خوشه‌بندی را مرور می‌کنیم.\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNQ3GDorgjTB"
      },
      "source": [
        "![](https://miro.medium.com/max/1400/1*ghEzFd4sMX37OvH_U1xPZQ.png)\n",
        "\n",
        "<a href=\"https://towardsdatascience.com/\">pic source</a>\n",
        "\n",
        "![](https://miro.medium.com/max/1400/1*Ntef_OJUpkrxytutzwDtIA.png)\n",
        "\n",
        "<a href=\"https://towardsdatascience.com/a-brief-introduction-to-unsupervised-learning-20db46445283\">pic source</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9F_0GGI5gjTD"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2\">\n",
        "<font color=\"red\" size=5>**2.K-means**</font>\n",
        "<br>\n",
        "    <br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-1\">\n",
        "<font color=\"red\" size=4>**2-1.معرفی الگوریتم**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0B5FiecgjTE"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "این الگوریتم با فرض وجود k خوشه، k مرکز خوشه را مشخص می‌کند و هر داده را به خوشه ای که داده به مرکز آن خوشه نزدیک‌تر است نسبت می‌دهد.\n",
        "<br>\n",
        "به عبارت دیگر به ازای k مرکز خوشه‌ی $c_k$ و $x_n$ هایی که عضو هر خوشه هستند هدف k-means کمینه کردن مقدار زیر است.\n",
        "<br>\n",
        "<div dir=ltr>\n",
        "$\\sum_{n=1}^{N} \\sum_{x_n \\in c_k}{||x_n – c_k||}^2$\n",
        "    \n",
        "<div dir=rtl>\n",
        "<br>\n",
        "<br>\n",
        "فرض کنید مرکز خوشه‌ها را در اختیار داریم، بنابراین یافتن اینکه هر داده به کدام خوشه متعلق است بسیار ساده است و کافی است ببینیم هر داده به کدام مرکز خوشه نزدیک‌تر است.\n",
        "<br>\n",
        "حال فرض کنید مرکز خوشه‌ها را نداریم؛ ولی برچسب هر داده در اختیار ماست و میدانیم هر داده متعلق به چه خوشه ای است، به این ترتیب می‌توانیم میانگین داده‌های هر خوشه را به عنوان مرکز آن خوشه معرفی کنیم.\n",
        "<br>\n",
        "<br>\n",
        "اما در ابتدای امر ما نه مرکز خوشه‌ها را داریم و نه برچسب داده هارا پس رویکرد چه باید باشد؟ Kmeans به این گونه عمل می‌کند: \n",
        "<br>\n",
        "ابتدا k مرکز خوشه‌ی تصادفی انتخاب می‌کند (درواقع  به طور تصادفی K تا از داده‌ها را به عنوان مراکز خوشه انتخاب می‌کند)\n",
        "<br>\n",
        " سپس با استفاده از مرکزهای انتخاب شده، خوشه‌ی هر داده را مشخص می‌کنیم\n",
        "<br>\n",
        " سپس با استفاده از برچسب‌های مشخص شده مرکزهای جدید را محاسبه و به روز رسانی می‌کنیم\n",
        "<br>\n",
        " همین دو مرحله را تا زمانی ادامه می‌دهیم که مرکز خوشه‌ها دیگر جابجا نشوند. \n",
        "<br>\n",
        "<br>\n",
        "نکته‌ی قابل این است که الگوریتم بعد از تعدادی مرحله (که اصولاً هم کم است) حتماً همگرا می‌شود و تا ابد نوسان نخواهد داشت. (این موضوع با این حقیقت قابل اثبات است که در هر مرحله mean squared distance بین داده‌ها و نزدیک‌ترین مرکز خوشه به آن‌ها تنها می‌تواند کاهش یابد)\n",
        "<br>\n",
        "<br>\n",
        "عملکرد الگوریتم k-means را میتوانید در مثال زیر ببینید:\n",
        "  <br>  \n",
        "      <br>  \n",
        "(اگر وب پیج در نوت بوک برای شما باز نمیشود، میتوانید مستقیما از طریق لینک به وب پیج دسترسی پیدا کنید)\n",
        "    \n",
        "    http://shabal.in/visuals/kmeans/2.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LOoPwHmwgjTF",
        "outputId": "af89f8c7-d7e1-4c33-f3d4-29e32103bbfa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<iframe src=\"http://shabal.in/visuals/kmeans/2.html\" width=\"900\" height=\"900\"></iframe>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%%html\n",
        "<iframe src=\"http://shabal.in/visuals/kmeans/2.html\" width=\"900\" height=\"900\"></iframe>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJp6GJ3zgjTI"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-2\">\n",
        "<font color=\"red\" size=4>**2-2.پیاده سازی k-means**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPq9MEAbgjTJ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "ابتدا اجازه دهید داره های مورد استفاده را معرفی کنیم. gene expression cancer RNA-Seq DataSet یک دیتاست است که ویژگی های آن میزان بیان ژن های مختلف در افراد مبتلا سرطان است که هرکدام نوع متفاوتی از تومور (BRCA, KIRC, COAD, LUAD and PRAD) را دارند. \n",
        "<br>\n",
        "لینک دیتابیس برای اطلاعات بیشتر :https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq\n",
        "<br>\n",
        "<br>\n",
        "نکته 1: دیتاست اصلی شامل تعداد 20531  ژن و میزان بیان هر کدام به ازای هر بیمار است (در واقع میزان بیان هر ژن یک ویژگی است پس دیتاست اصلی 20531 ویژگی به ازای هر نمونه دارد) ، چون میخواهیم یک مثال ملموس داشته باشیم و داده ها قابل visulisation باشند، داده ها را با استفاده از TSNE کاهش بعد داده ایم اما این مراحل به دلیل اینکه مبحث مورد بحث ما نیست در این قسمت نیامده است. نتیجه ی نهایی پس از کاهش بعد یک دیتاست است که به ازای هر نمونه دو ویژگی دارد.\n",
        "<br>\n",
        "<br>\n",
        "نکته 2 : این دیتاست یک دیتاست با برچسب است اما ما قرار نیست از برچسب ها در طی خوشه بندی استفاده کنیم چون خوشه بندی یک رویکرد بدون ناظر است. بعد از خوشه بندی بدون استفاده از برچسب ها، ما نتایج خوشه بندی را با برچسب های واقعی مقایسه میکنیم تا این دید را منتقل کنیم که در کاربرد های واقعی نتیجه ی حاصل از خوشه بندی میتواند یک نتیجه ی معنا دار باشد اما طبیعتا در کاربرد های واقعی ما برچسب ها را نخواهیم داشت.\n",
        "<br>\n",
        "<br>\n",
        "پس نهایتا داده های مورد استفاده برای مثال خوشه بندی به شکل زیر هستند، تعداد 640 بیمار که به ازای هر کدام 2 ویژگی مرتبط به ژن ها داریم و میخواهیم ببینیم با توجه به تفاوت این ویژگی ها در افراد، آیا میشود آن ها را در خوشه های مجزا قرار داد ؟\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBi2_WEHgjTK"
      },
      "outputs": [],
      "source": [
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        "label=['KIRC', 'KIRC', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'BRCA', 'LUAD', 'KIRC', 'PRAD', 'BRCA', 'PRAD', 'COAD', 'LUAD', 'COAD', 'BRCA', 'PRAD', 'PRAD', 'KIRC', 'BRCA', 'BRCA', 'COAD', 'KIRC', 'KIRC', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC', 'PRAD', 'BRCA', 'BRCA', 'LUAD', 'PRAD', 'BRCA', 'KIRC', 'KIRC', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'COAD', 'COAD', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC', 'LUAD', 'BRCA', 'KIRC', 'BRCA', 'PRAD', 'COAD', 'BRCA', 'PRAD', 'BRCA', 'COAD', 'LUAD', 'BRCA', 'LUAD', 'LUAD', 'COAD', 'BRCA', 'COAD', 'BRCA', 'KIRC', 'LUAD', 'PRAD', 'COAD', 'PRAD', 'KIRC', 'PRAD', 'KIRC', 'COAD', 'BRCA', 'PRAD', 'LUAD', 'BRCA', 'KIRC', 'BRCA', 'LUAD', 'LUAD', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'BRCA', 'PRAD', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC', 'PRAD', 'BRCA', 'PRAD', 'LUAD', 'BRCA', 'BRCA', 'BRCA', 'BRCA', 'KIRC', 'LUAD', 'BRCA', 'BRCA', 'BRCA', 'LUAD', 'BRCA', 'BRCA', 'KIRC', 'KIRC', 'BRCA', 'LUAD', 'BRCA', 'KIRC', 'KIRC', 'LUAD', 'BRCA', 'LUAD', 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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvVRoAy0gjTL"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    در زیر نمونه ها را بدون برچسب هایشان رسم میکنیم و به طور شهودی میتوان دید داده ها به 5 خوشه ی مجزا قابل تفکیک اند."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z1XLOkHjgjTM",
        "outputId": "dad11ec2-ed00-44d3-bc86-d20c1963da54"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# plotting data wrt. its features\n",
        "X=np.array(data)\n",
        "A=X.T[0]\n",
        "B=X.T[1]\n",
        "plt.scatter(A,B)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MciG2h4egjTM"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    سپس یک مدل kmeans با تعداد 5 خوشه را روی این داده ها آموزش میدهیم و اندیس خوشه ای که هر نمونه به آن منتسب شده را نمایش میدهیم.\n",
        "    <br>\n",
        "  توجه کنید که اندیس خوشه را با برچسب داده اشتباه نگیرید. ما روی داده ها برچسب نزده ایم که هرکدام به چه نوع سرطانی تعلق دارند. صرفا نمونه ها را خوشه بندی کردیم و گفتیم هر کدام متعلق به کدام خوشه هستند."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1J6uxPuVgjTN",
        "outputId": "cf5249ea-3fce-43d9-9cdd-63c9d98ebf7b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 1, 2, 0, 2, 2, 2, 3, 1, 0, 2, 0, 4, 3, 4, 2, 0, 0, 1, 2, 2, 4,\n",
              "       1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 0, 2, 2, 3, 0, 2, 1, 1, 2, 0, 2, 2,\n",
              "       4, 4, 2, 2, 2, 2, 1, 3, 2, 1, 2, 0, 4, 2, 0, 2, 4, 3, 2, 3, 3, 4,\n",
              "       2, 4, 2, 1, 3, 0, 4, 0, 1, 0, 1, 4, 2, 0, 3, 2, 1, 2, 3, 3, 3, 2,\n",
              "       2, 1, 2, 0, 2, 2, 2, 2, 1, 0, 2, 0, 3, 2, 2, 2, 2, 1, 3, 2, 2, 2,\n",
              "       3, 2, 2, 1, 1, 2, 3, 2, 1, 1, 3, 2, 3, 3, 3, 3, 0, 2, 1, 0, 4, 1,\n",
              "       2, 3, 1, 1, 2, 2, 3, 2, 2, 0, 0, 2, 4, 3, 3, 1, 1, 0, 2, 3, 1, 2,\n",
              "       3, 1, 4, 2, 3, 2, 3, 0, 1, 4, 2, 2, 4, 0, 1, 1, 2, 2, 1, 0, 0, 3,\n",
              "       3, 0, 4, 2, 0, 1, 2, 1, 2, 2, 4, 3, 2, 3, 1, 4, 1, 2, 0, 0, 1, 3,\n",
              "       4, 1, 3, 4, 3, 2, 3, 4, 0, 2, 2, 3, 1, 3, 2, 1, 2, 4, 3, 2, 3, 2,\n",
              "       0, 0, 1, 2, 1, 3, 0, 0, 2, 3, 4, 2, 2, 1, 3, 3, 2, 1, 2, 4, 3, 0,\n",
              "       2, 1, 4, 3, 3, 4, 0, 2, 2, 1, 0, 2, 1, 2, 1, 2, 3, 3, 2, 0, 3, 4,\n",
              "       1, 2, 4, 2, 3, 4, 2, 4, 0, 4, 4, 2, 2, 4, 3, 2, 3, 0, 4, 0, 2, 2,\n",
              "       2, 3, 2, 1, 2, 1, 0, 0, 2, 2, 4, 2, 0, 2, 0, 3, 1, 2, 0, 0, 2, 4,\n",
              "       2, 3, 4, 3, 2, 0, 0, 2, 0, 3, 3, 0, 3, 4, 2, 0, 2, 2, 4, 2, 2, 4,\n",
              "       0, 2, 2, 3, 2, 1, 2, 2, 0, 0, 0, 4, 3, 1, 1, 2, 1, 3, 2, 4, 2, 3,\n",
              "       2, 2, 2, 3, 2, 2, 1, 1, 1, 0, 3, 2, 2, 1, 2, 1, 0, 3, 3, 2, 2, 0,\n",
              "       2, 1, 2, 3, 2, 0, 2, 1, 2, 4, 3, 0, 1, 1, 1, 2, 2, 3, 2, 3, 2, 0,\n",
              "       1, 2, 2, 3, 1, 3, 2, 3, 2, 1, 0, 1, 0, 1, 3, 3, 1, 0, 0, 3, 1, 0,\n",
              "       1, 4, 2, 1, 1, 2, 1, 1, 3, 2, 0, 3, 2, 3, 2, 1, 0, 2, 3, 1, 2, 2,\n",
              "       4, 2, 2, 1, 0, 1, 2, 2, 2, 1, 0, 1, 1, 2, 2, 3, 2, 4, 0, 1, 1, 1,\n",
              "       2, 1, 0, 0, 2, 1, 2, 1, 3, 2, 1, 2, 1, 4, 3, 0, 3, 2, 2, 2, 3, 2,\n",
              "       4, 4, 2, 2, 2, 0, 2, 4, 0, 2, 2, 1, 1, 2, 2, 2, 2, 2, 3, 4, 4, 0,\n",
              "       4, 1, 3, 0, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 0, 0, 3, 3, 3, 2, 2, 2,\n",
              "       0, 0, 4, 2, 3, 1, 2, 3, 4, 1, 2, 1, 0, 4, 2, 0, 0, 2, 2, 0, 2, 2,\n",
              "       2, 3, 2, 2, 3, 0, 0, 1, 4, 2, 2, 4, 2, 1, 0, 2, 0, 2, 2, 0, 2, 1,\n",
              "       2, 1, 4, 4, 0, 2, 2, 0, 2, 1, 0, 0, 2, 3, 0, 3, 1, 2, 0, 2, 4, 3,\n",
              "       3, 3, 0, 3, 2, 2, 3, 0, 1, 1, 2, 3, 3, 0, 2, 0, 4, 0, 4, 1, 2, 0,\n",
              "       2, 1, 2, 2, 2, 0, 2, 3, 1, 0, 1, 1, 2, 0, 2, 2, 0, 2, 0, 3, 3, 2,\n",
              "       1, 0])"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=5).fit(data)\n",
        "y_pred=kmeans.predict(data)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnm-GzsVgjTN"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    نمونه ها را با توجه به خوشه هایشان رنگ آمیزی میکنیم، مراکز خوشه نیز در نمودار نشان داده شده اند."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nhni0KiZgjTN",
        "outputId": "85a03186-d610-415d-f7de-5f0c80527458"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "A=np.array(data).T[0]\n",
        "B= np.array(data).T[1]\n",
        "plt.scatter(A,B, c =y_pred)\n",
        "plt.scatter( kmeans.cluster_centers_.T[0], kmeans.cluster_centers_.T[1],color=\"red\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKZ4PqIegjTO"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    مراکز خوشه ها از طریق ` kmeans.cluster_centers_` قابل دسترسی هستند. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3BNgFXENgjTO",
        "outputId": "535c08c3-ef4a-41ac-8527-273c3e1b32ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ -7.71792019,   8.58312359],\n",
              "       [ 31.69665558,  -7.66848647],\n",
              "       [ 20.6374405 ,  22.03263534],\n",
              "       [-24.59613826, -19.93664994],\n",
              "       [  8.35913904, -20.8285965 ]])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        " kmeans.cluster_centers_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNvak-1vgjTO"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "   هم چنین میتوانیم برای داده های جدید مشخص کنیم به کدام خوشه متعلق اند. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0IQ2OyNbgjTO",
        "outputId": "2ca55d0d-82d4-4b38-e78c-044e72c58e17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 2, 3, 4])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_new = np.array([[0, 2], [15, 25], [-30, -15], [10,-10]])\n",
        "kmeans.predict(X_new)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXceyMyegjTP"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "  **نکته** : به جای اینکه به طور سخت تعیین کنیم هر نمونه متعلق به کدام خوشه است، میتوان فاصله ی آن ها با مراکز خوشه ها را نمایش داد به نوعی هرچقدر فاصله ی نمونه با مرکز خوشه ای کمتر از فاصله اش با سایر مرکز خوشه ها باشد، احتمال قرار گرفتنش در آن خوشه نسبت به دیگر خوشه ها بیشتر است و این یک رویکرد برای خوشه بندی نرم خواهد بود.\n",
        "    <br>\n",
        "    در ماتریس فاصله های زیر، میتوان هر سطر را به مجموع آن سطر تقسیم کرد و احتمالاتی بین 0 و 1 نیز داشت."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LQp8BUpWgjTP",
        "outputId": "de21f9f1-417b-4cef-c7fd-e5c6cabc235f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[10.14415143, 33.13846112, 28.76126613, 32.95734558, 24.31090339],\n",
              "       [28.02887313, 36.68798599,  6.37071334, 59.89287664, 46.3072488 ],\n",
              "       [32.44464207, 62.13073636, 62.73409329,  7.31930559, 38.79943408],\n",
              "       [25.67600394, 21.82156774, 33.75270163, 35.99485789, 10.95221105]])"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kmeans.transform(X_new)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdmMBfNHgjTP"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    چون در مثال ما برچسب ها را داشتیم دست به یک آزمایش میزنیم، در نهایت برچسب واقعی داده ها را نمایش میدهیم تا به یک شهود برسیم که واقعا هر خوشه ی یافت شده یک گونه ی سرطان متفاوت بوده است و میتوان نتیجه گرفت در کاربرد های واقعی هر خوشه میتواند تفاوت معناداری با سایر خوشه ها داشته باشد."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wk7yugSjgjTP",
        "outputId": "c343ea32-2eca-4118-aff3-0606677c852a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "col={\"BRCA\":\"blue\", \"KIRC\":\"red\", \"COAD\":\"green\", \"LUAD\":\"yellow\" , \"PRAD\":\"black\"}\n",
        "YC=[]\n",
        "for i in range(len(label)):\n",
        "    YC.append(col[label[i]])\n",
        "plt.scatter(A,B, c =YC)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cGox2PdmgjTQ"
      },
      "source": [
        "\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-3\">\n",
        "<font color=\"red\" size=4>**2-3.مقداردهی اولیه ی مراکز خوشه ها**</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuGmAyykgjTQ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "یک چالش پیش رو در استفاده از این الگوریتم این است که هرچند الگوریتم تضمین می‌کند همگرا شود، نمبتواند تضمین کند که به بهترین پاسخ (بهینه‌ی سراسری) همگرا می‌شود و پاسخ الگوریتم می‌تواند بسته به مقدار دهی اولیه‌ی مرکز خوشه‌ها می‌تواند متفاوت باشد و به بهینه‌های محلی همگرا شود.\n",
        "<br>\n",
        "<br>\n",
        "برای روبرویی با این چالش چند روش برای مقداردهی اولیه‌ی مرکز خوشه‌ها پیشنهاد شده است که در ادامه مورد بررسی قرار می‌دهیم.\n",
        "<br>\n",
        "    <br>\n",
        "**الف)** اگر به طریقی یک تقریب از مرکز خوشه‌ها داشته باشیم که از حالت رندوم بهتر باشد (مثلاً قبلاً یک الگوریتم خوشه‌بندی دیگر را اجرا کرده باشیم) می‌توانیم به طور دستی آن را به جای مقادیر رندوم، به عنوان مقادیر اولیه‌ی مراکز خوشه‌ها ست کنیم. به این منظور میتوان مراکز خوشه پیشنهادی را در یک np.array ذخیره کرد و با استفاده از هایپرپارامتر `init` مقدار اولیه ی مراکز خوشه را ست کرد. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6nCVG6vzgjTQ"
      },
      "outputs": [],
      "source": [
        "good_init = np.array([[8, -20], [-8, 7], [19, 25], [-25, -20], [30, -10]])\n",
        "kmeans = KMeans(n_clusters=5, init=good_init, n_init=1).fit(data)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1N1_yeVgjTQ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "**ب)** راه حل دیگر این است که الگوریتم‌ها به تعداد دفعات معین اجرا کنیم و از میان آن‌ها بهترین پاسخ را انتخاب کنیم. واضح است برای انجام این کار نیاز داریم معیاری برای مقایسه‌ی عملکرد داشته باشیم (performance metric)تا بتوانیم بهترین آن‌ها را انختاب کنیم. به این معیار model interia گفته می‌شود که عبارت است از میانگین مجذورات فاصله بین داده‌ها و مرکز خوشه‌هایشان است که طبیعتا هرچقدر این مقدار کمتر باشد یعنی مدل بهتر بوده. \n",
        "به این منظور میتوان هایپرپارامتر `n_init` را به تعداد دفعاتی که میخواهیم الگوریتم اجرا شود ست کنیم.\n",
        "<br>\n",
        "<br>\n",
        "    توجه : اگر این هایپرپارامتر را ست نکنیم مقدار پیش فرض آن 10 خواهد بود.\n",
        "    <br>\n",
        "    **نکته**: با استفاده از `kmeans.inertia_` میتوان به inertiaی مدل نهایی دست پیدا کرد. هم چنین `score` برای داده ای که خوشه بندی روی آن انجام شده همواره برابر با -inertia خواهد بود. زیرا score طبق تعریف باید اینگونه باشد که بیشتر بودنش به معنای بهتر بودنش باشد."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x7glCQZzgjTR",
        "outputId": "570e511e-1e9e-438d-d677-fd393fc11c03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Interia: 14489.701572428849      Score: -14489.701572428849\n"
          ]
        }
      ],
      "source": [
        "kmeans = KMeans(n_clusters=5, n_init=20).fit(data)\n",
        "print(\"Interia:\",kmeans.inertia_,\"     Score:\",kmeans.score(X))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRLiHQ4YgjTR"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-4\">\n",
        "<font color=\"red\" size=4>**2-4.بهبودهای K-means**</font>\n",
        "   <br>\n",
        "    <br>\n",
        "    <font face=\"XB Zar\" size=4><div dir=rtl id=\"2-4-1\">\n",
        "<font color=\"red\" size=4>2-4-1.K-means++</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IB9XYzcxgjTR"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "یکی از بهترین بهبودهای پیشنهاد شده برای kmeans الگوریتم  kmeans++است که با داشتن یک روش هوشمندانه‌تر برای مقدار دهی اولیه‌ی مراکز خوشه می‌تواند احتمال رسیدن به پاسخ‌های sub-optimal را افزایش دهد. ایده‌ی کلی این است که مراکز خوشه به گونه ای انتخاب شوند که از یکدیگر دور باشند. نشان داده شده اجرای این بخش به طرز شایان توجهی تعداد دفعات اجرای الگوریتم برای رسیدن به جواب بهنیه را کم می‌کند و از همین جهت محاسبات اضافه‌ی لازم برای این مقداردهی اولیه، ارزشمند است.\n",
        "<br>\n",
        "<br>\n",
        "الگوریتم به شکل زیر عمل می‌کند:\n",
        "<br>\n",
        "یک نمونه‌ی رندوم را به عنوان مرکز خوشه‌ی اول $c^{(1)}$ انتخاب کن\n",
        "<br>\n",
        "تا  زمانی که تمام k مرکز خوشه انتخاب شوند :\n",
        "<br>\n",
        "نمونه‌ی $x^{(i)}$ را با احتمال $D(x^{(i)})^2/sum_{j=1}^{m} D(x^{(j)})^2 $ به عنوان مرکز خوشه‌ی بعدی $c^{(i)}$ انتخاب کن. که $D(x^{(i)})$ فاصله‌ی نمونه‌ی $x^{(i)}$ با نزدیک‌ترین مرکز خوشه‌ی تا به حال انتخاب شده است.\n",
        "<br>\n",
        "    <br>\n",
        "مشخص است که سیاست در انتخاب مرکز خوشه‌ها به گونه ای است که نمونه‌هایی که از مراکز خوشه‌های فعلی انتخاب شده فاصله‌ی بیشتری را دارند، به مراتب احتمال بیشتری برای انتخاب شدن دارند.\n",
        "<br>\n",
        "    <br>\n",
        "K-means استفاده شده در کدهای بالا به طور پیش فرض از همین روش برای مقدار دهی اولیه استفاده می‌کند، درصورت نیاز به مقدار دهی اولیه‌ی رندم باید هایپرپارامتر `init` را برابر با `random` ست کنیم.\n",
        "<br>\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOMhZgRqgjTS"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-4-2\">\n",
        "<font color=\"red\" size=4>2-4-2.K-means تسریع شده</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o83knTX3gjTS"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "این الگوریتم با تلاش برای عدم انجام محاسبات غیر ضروری تا حد قابل توجهی الگوریتم اصلی را تسریع می‌کند. \n",
        "<br>\n",
        "رویکرد کلی استفاده از نامساوی مثلثی و در نظر گرفتن کران‌های بالا و پایین برای فاصله‌ی نمونه‌ها و مراکز خوشه است.\n",
        "<br>\n",
        "kmeans استفاده شده در کدهای بالا به طور پیش فرض از همین روش استفاده می‌کند، درصورت نیاز به اجرای الگوریتم به روش اصلی، باید هایپرپارامتر `algorithm` را برابر با `full` ست کنیم.\n",
        "    \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HJdcPkagjTS"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-4-3\">\n",
        "<font color=\"red\" size=4>2-4-3.mini-batch K-means</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aje2qCUxgjTS"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "این الگوریتم k means را قادر می‌سازد به جای استفاده از تمام نمونه‌ها در هر iteration، فقط از یک دسته یا mini batch از داده‌ها برای آپدیت کردن و جابجا کردن مراکز خوشه استفاده کند که در حدود 3 تا 4 بار الگوریتم را تسریع می‌کند. هم چنین استفاده از Mini batch این مزیت را دارد که الگوریتم برای داده‌های عظیم که برای جا شدن تمام نمونه هایشان در مموری مشکل دارند نیز قابل اجرا خواهد بود.\n",
        "به این منظور به شکل زیر عمل میکنیم."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "slB4VDorgjTS",
        "outputId": "b3187f83-519b-4bf8-d27f-3c513d7c4175"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MiniBatchKMeans(n_clusters=5)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.cluster import MiniBatchKMeans\n",
        "minibatch_kmeans = MiniBatchKMeans(n_clusters=5)\n",
        "minibatch_kmeans.fit(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HIEJPLDgjTT"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "هرچند، استفاده از mini batch باعث افزایش سرعت بسیار زیادی می‌شود؛ اما در اکثر مواقع inertia به مقدار کمی نسبت به حالت عادی بیشتر خواهد بود. این تفاوت خصوصاً با افزایش تعداد خوشه‌ها نمایان‌تر می‌شود.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNJVuHtXgjTT"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-5\">\n",
        "<font color=\"red\" size=4>**2-5.انتخاب تعداد خوشه‌ها**</font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwNFautRgjTT"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "تا اینجا ما فرض کردیم تعداد خوشه‌ها را میدانیم و در مثال‌های فوق تعداد خوشه‌ها از نگاه کردن به نمونه‌ها قابل تشخیص بود؛ اما برای کاربردهای واقعی این موضوع صادق نخواهد بود. اگر k بیش از حد زیاد یا کم انتخاب شود خوشه‌بندی مناسبی را نخواهیم داشت پس مهم است روشی برای یافتن مقدار مناسب k داشته باشیم.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-GkalGPgjTT"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-5-1\">\n",
        "<font color=\"red\" size=4>2-5-1.استفاده از inertia</font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsUOgwyhgjTT"
      },
      "source": [
        "![](https://media.geeksforgeeks.org/wp-content/uploads/20190606105550/distortion1.png)\n",
        "\n",
        "<a href=\"https://www.geeksforgeeks.org/elbow-method-for-optimal-value-of-k-in-kmeans/\">pic source</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmc3DLCMgjTT"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "ممکن است در ابتدا اینگونه به نظر برسد که k ای که منجر به مدلی با inertia کمتر شود، k مناسب تری خواهد بود؛ اما متاسفانه این موضوع درست نیست. هرچقدر k یا همان تعداد خوشه‌ها افزایش یابد، Inertia کاهش میابد؛ زیرا تعدا مراکز خوشه بیشتر خواهد بود و فاصله‌ی هر نمونه با نزدیک‌ترین مرکز خوشه اش کمتر خواهد شد تا حدی که اگر به اندازه‌ی تعداد نمونه‌ها خوشه داشته باشیم هر نمونه مرکز خوشه‌ی خود خواهد بود و Inertia=0 را خواهیم داشت. پس کم‌تر بودن Inertia به معنای k مناسب‌تر نیست.\n",
        "<br>\n",
        "<br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "به نمودار بالا دقت کنید علاوه بر اینکه با افزایش تعداد خوشه‌ها Inertia کاهش میابد یک موضوع دیگر را نیز می‌توان از نمودار برداشت کرد. از مقدار k=1 to 4 مقدار inertia با شدت چشمگیری در حال کاهش است در حالی که بعد از 4 کاهش بسیار آهسته‌تر می‌شود. این بدان معناست که کاهش inertia از 4 به بعد تنها به دلیل افزایش تعداد خوشه هاست اما از قبل از آن این تفاوت معنی دار بوده است و در واقع افزایش تعداد خوشه‌ها در آن مراحل gain زیادی برای ما دارد. در واقع نمودار به شکل یک دست خواهد بود که قسمت شکستگی نمودار که شبیه آرنج است (یعنی جایی که شدت کاهش کم می‌شود) می‌تواند انتخاب خوبی برای تعداد خوشه‌ها باشد.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2Az0RaygjTT"
      },
      "source": [
        "\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-5-2\">\n",
        "<font color=\"red\" size=4>2-5-2.silhouette score </font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1Dsu4AzgjTU"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "یک روش دقیق‌تر و البته با محاسبات بیشتربرای تعیین بهترین تعداد خوشه، استفاده از silhouette score است.\n",
        "<br>\n",
        "Silhouette score برابر است با میانگین silhouette coefficient روی تمام نمونه‌ها.\n",
        "<br>\n",
        "$Silhouette-coefficient = {(b-a)}/{max(a,b)}$\n",
        "<br>\n",
        "که در آن a میانگین فاصله‌ی نمونه با تمام نمونه‌های هم خوشه اش است (میانگین فاصله‌ی درون خوشه ای) و b میانگین فاصله‌ی نمونه با نمونه‌های نزدیک‌ترین خوشه است (درواقع b کمترین میانگین فاصله با نمونه‌های خوشه‌های دیگراست).\n",
        "<br>\n",
        "    <br>\n",
        "مقدار silhouette coefficient می‌تواند بین+1 و -1 باشد که مقدار نزدیک به +1 نشان می‌دهد نمونه به نمونه‌های درون خوشه‌ی نسبت یافته اش نزدیک و از نمونه‌های سایر خوشه‌ها دور است که در واقع یعنی به درستی خوشه اش انتخاب شده و مقدار نزدیک به -1 می‌تواند بیانگر این باشد که نمونه به خوشه‌ی اشتباهی نسبت داده شده است.\n",
        "<br>\n",
        "  به شکل زیر میتوان silhouette score برای داده ها را محاسبه کرد. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmqCE-L1gjTU",
        "outputId": "f08e6dbe-74c3-4f31-eaad-cfcd37a7e62a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8062882462874054"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "silhouette_score(data, kmeans.labels_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW1RJSasgjTU"
      },
      "source": [
        "![](https://aiaspirant.com/wp-content/uploads/2019/07/Silhouette.png)\n",
        "\n",
        "\n",
        "<a href=\"https://aiaspirant.com/optimal-k-in-k-menas/\">pic source</a>\n",
        "\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "به این ترتیب می‌توانیم Silhouette score را برای k های متفاوت محاسبه و مقایسه کنیم و Silhouette score بیشتر به معنای k مناسب‌تر خواهد بود. هم چنین استقاده از Silhouette score بر خلاف inertia به ما این امکان را می‌دهد که دو k ی دلخواه را نسبت به هم مقایسه کنیم (در روش قبل فقط k بهینه را با استفاده از محل شکستگی پیدا می‌کردیم).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EN163iOPgjTU"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "علاوه بر نمایش silhouette score به ازای k های مختلف می‌توان از silhouette diagram نیز استفاده کرده که در آن silhouette coefficient برای تمام نمونه‌ها به ترتیب شماره‌ی خوشه شان و سپس ترتیب بزرگی silhouette coefficient نمایش داده می‌شود. خط عمودی در این نمودار نیز silhouette score به ازای k ای است که خوشه‌بندی با ان انجام شده."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soNHs9zogjTU"
      },
      "source": [
        "\n",
        "![](https://scikit-learn.org/stable/_images/sphx_glr_plot_kmeans_silhouette_analysis_003.png)\n",
        "\n",
        "\n",
        "<a href=\"https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\">pic source</a>\n",
        "\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        " اگر silhouette coefficient خوشه‌هایی با خط عمودی فاصله داشته باشند نشان دهنده‌ی این است که خوشه‌بندی بد انجام شده و خوشه از دیگر خوشه‌ها فاصله‌ی کافی ندارند.\n",
        "<br>\n",
        "<br>\n",
        "نکته : از این نمودار میشود اندازه ی نسبی خوشه ها نسبت به یکدیگر را نیز دید. در شرایطی که برای دو k شرایط تقریبا یکسان بود یک انتخاب خوب میتواند انتخاب k ای باشد که با استفاده از آن اندازه ی خوشه ها در آن هم اندازه تر هستند."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wKtienFIgjTU"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-5-3\">\n",
        "<font color=\"red\" size=4>2-5-3.DB index </font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pzh10tDugjTV"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "یک روش دیگه برای سنجیدن کیفیت خوشه بندی انجام شده Davies-Bouldin index است.\n",
        "<br>\n",
        "تعاریف زیر را در نظر بگیرید:\n",
        "<br>\n",
        "<br>\n",
        "پراکندگی خوشه :  میتواند به صورت یک انحراف معیار تعمیم یافته تعبیر شود.\n",
        "<br>\n",
        "<div dir=ltr>\n",
        "$\\delta_k:= \\sqrt{\\frac{1}{N_k}\\sum_{x_n\\in c_k}||x_n-c_k||^2}$\n",
        "<br>\n",
        "<br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "شباهت خوشه ها : دو خوشه شبیه در نظر گرفته میشوند اگر نسبت به فاصله شان پراکندگی زیادی داشته باشند.\n",
        "<br>\n",
        "<div dir=ltr>\n",
        "$S_{kl}:=\\frac{\\delta_k+\\delta_l}{||c_k-c_l||}$\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "<br>\n",
        "<br>\n",
        "با توجه به تعاریف بالا میتوان دید  DB-index  که به صورت زیر تعریف میشود میتواند معیار خوبی برای سنجش کیفیت خوشه بندی باشد.\n",
        "<br>\n",
        "<div dir=ltr>\n",
        "$V_{DB}:=\\frac{1}{k}\\sum_{k=1}^K \\underset{l\\neq k}{max}S_{kl}$\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJZz02kCgjTV"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2-6\">\n",
        "<font color=\"red\" size=4>**2-6.محدودیت‌های k-means** </font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCfaM43cgjTV"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "علی رغم ویژگی‌های مثبت kmeans مانند سریع و مقیاس پذیر بودنش، این الگوریتم محدودیت‌های قابل توجهی دارد از جمله:\n",
        "<br>\n",
        "-\tوابستگی به مقدار اولیه و نیاز به چندین بار اجرا با مقادیر اولیه متفاوت\n",
        "-\tنیاز به مشخص کردن تعداد خوشه‌ها\n",
        "-\tمشکل در خوشه‌بندی نمونه‌هایی که خوشه‌هایشان هم اندازه نیستند\n",
        "-\tمشکل در خوشه‌بندی نمونه‌هایی که چگالی خوشه‌ها در آن متفاوت است\n",
        "-\tمشکل در خوشه‌بندی نمونه‌هایی که خوشه‌ها فرم غیر کروی (غیر دایره ای) دارند\n",
        "<br>\n",
        "<br>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "نکته: برای مشکل آخر، می‌توان با استفاده از اسکیل کردن ویژگی‌ها، فرم خوشه‌ها را به فرم کروی نزدیک‌تر کرد. هرچند تضمین صد در صدی وجود نداره که اسکیل کردن ویژگی‌ها تمام خوشه‌ها را ایده آل کند؛ اما معمولاً باعث بهبود عملکرد می‌شود.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1-PcyVdgjTV"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"3\">\n",
        "<font color=\"red\" size=5>**3.کاربردهای خوشه بندی** </font>\n",
        "    <br>\n",
        "    <br>\n",
        "    <font face=\"XB Zar\" size=4><div dir=rtl id=\"3-1\">\n",
        "<font color=\"red\" size=4>**3-1. Image Segmentation** </font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhttenIngjTW"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "هدف کلی Image segmentation این است که یک تصویر به بخش‌هایی تقسیم شود که هرکدام از این بخش‌ها یک موجودیت مجزای مورد نظر باشد. مثال: تشخیص موانع در اتوموبیل های خودران\n",
        "<br>\n",
        "<br>\n",
        "هرچند مدل‌های Image segmentation برای مثال‌هایی مانند اتوموبیل خودران می‌توانند بسیار پیچیده باشد و به عنوان مثال نیاز به شبکه‌های عصبی و ... باشد، در موارد ساده‌تر می‌توان رویکرد ساده تری به نام Color segmentation را استفاده کرد. در این رویکرد ما بخش‌هایی از تصویر را که رنگ تقریباً مشابهی دارند به عنوان یک گروه مجزا (یک خوشه) جدا می‌کنیم و پیش فرض ما این است که قسمت‌های همرنگ تصویر احتمالاً متعلق به یک موجودیت هستند. \n",
        "<br>\n",
        "<br>\n",
        "این رویکرد ساده در برخی مثال‌ها می‌تواند راهگشا باشد برای مثال تشخیص اینکه چند درصد از یک تصویر ماهواره ای از زمین، جنگل و چند درصد اقیانوس است. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsFKA5U6gjTW"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    بیایید مثال تشحیص درصد اقیانوس و جنگل را اجرا کنیم. در مثال زیر ما یک تصویر ماهواره ای داریم و با استفاده از خوشه بندی در واقع هر پیکسل از تصویر را به یک خوشه اختصاص میدهیم که از نظر رنگ کمترین فاصله را با مرکز خوشه اش دارد و سپس تمام اعضای هر خوشه را با میانگین مقدار خوشه جایگزین میکنیم. \n",
        " <br>\n",
        "    لود کردن تصویر و نمایش  :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "ul3jym3UgjTW",
        "outputId": "38996b25-c140-4fd2-e6d0-4b277ffe3c50"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib.image import imread\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "image = imread(\"./map.jpg\")\n",
        "\n",
        "plt.imshow((image).astype(np.uint8))\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drtLAwQcgjTW"
      },
      "source": [
        "<a href=\"http://www.deviantart.com/giresun/art/turkey-satellite-view-18839885\">pic source</a>\n",
        "\n",
        "\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    خوشه بندی تصویر با k=3 (با دانش قبلی که تصویر ماهواره ای اصولا از سه بخش خشکی آب و جنگل تشکیل شده) و سپس جاگذاری هر پیکسل با میانگین مقدار خوشه اش:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ea-eAkhBgjTX"
      },
      "outputs": [],
      "source": [
        "X = image.reshape(-1, 3)\n",
        "kmeans = KMeans(n_clusters=3).fit(X)\n",
        "segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
        "segmented_img = segmented_img.reshape(image.shape)\n",
        "colors=kmeans.labels_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7VYd9xDgjTX"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    نمایش تصویر پس از خوشه بندی\n",
        "    میتوان دید به خوبی جنگل ها در یک خوشه، آب ها در یک خوشه و خشکی ها در یک خوشه قرار گرفتند."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLUPAzQKgjTX",
        "outputId": "113c2008-eee0-46e8-b70d-abce72d4a41a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "out = segmented_img\n",
        "plt.imshow((out).astype(np.uint8))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6re_emGgjTY"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    نمایش درصد هر خوشه :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FnGliKJ_gjTY",
        "outputId": "c57a8380-a00a-481e-c6a4-68477ae364a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cluster0: 0.293452066655489 %\n",
            "Cluster1: 0.47722637491335235 %\n",
            "Cluster2: 0.22932155843115862 %\n"
          ]
        }
      ],
      "source": [
        "l=list(colors)\n",
        "print(\"Cluster0:\",l.count(0)/len(l),\"%\\nCluster1:\",l.count(1)/len(l),\"%\\nCluster2:\",l.count(2)/len(l),\"%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WxbBRdGgjTY"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    **نکته**: همانطور که گفته شد ما تعداد خوشه هارا با استفاده از کاربرد خود و دانش قبلی انتخاب کردیم. در مثال های دیگر نیز باید با بررسی های قبلی تعداد خوشه ها را انتخاب کنیم. اگر تعداد خوشه ها کم باشد دو موجودیت مجزا در تصویر که میخواستیم تفکیکشان کنیم در یک دسته قرار خواهند گرفت مثلا اگر تعداد خوشه ها در بالا 2 باشد جنگل و آب ها در یک خوشه ادغام شده و نمیتوانیم درصد آن ها را مشخص کنیم. مشخص است که اگر خوشه ها بیش از حد زیاد انتخاب شوند نیز مطلوب نیست."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHfjqfMPgjTZ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"3-2\">\n",
        "<font color=\"red\" size=4>**3-2. پیش پردازش داده‌ها** </font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2O5LAjPkgjTZ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "یکی از پیش پردازش‌های مهم **کاهش ابعاد** است. ما با کاهش ابعاد سعی می‌کنیم ابعاد داده‌های خود را کمتر کنیم به گونه ای که داده در ابعاد کمتر همچنان اطلاعات کافی برای بیان داده‌ها را دارا باشد به همین علت اصولاً داده‌ها در ابعاد پایین‌تر، اطلاعات نامفید و اضافی که ممکن است داده‌ی اولیه داشته باشد را نخواهند داشت و البته طبیعتا بخشی از اطلاعات اصلی داده را از دست میدهند که اگر متد ما خوب نباشد آن بخش ها میتواند بخش های مفیدی بوده باشند.\n",
        "<br>\n",
        "<br>\n",
        "یک روش کاهش ابعاد استفاده از خوشه‌بندی است به این شکل که ما داده‌ها را خوشه‌بندی کنیم و هر داده را با وکتور فاصله اش از تمام مرکز داده‌ها جایگزین کنیم(در مثال های بالا دیدیم که میتوان فاصله ی هر نمونه با مرکز خوشه را پس از خوشه بندی داشت). به این شکل ابعاد داده‌ی خود را از بعد اولیه‌ی n به k که تعداد خوشه هاست کاهش دادیم (n>k) و میدانیم این بیان جدید داده معنا دار است؛ چون هر خوشه داده‌های شبیه به هم را در خود دارد و فاصله از هر خوشه بیانگر شباهت یا تفاوت نمونه نسبت به نمونه‌ی سایر خوشه هاست و درواقع این بیان جدید از داده‌ها، دور یا نزدیک بودن آن‌ها نسبت به هم و ارتباطشان نسبت به یکدیگر را در خود حفظ کرده است.\n",
        "<br>\n",
        "<br>\n",
        "مزیت کاهش ابعاد داده‌ها این است که اولاً حجم داده‌ها کم می‌شود و منابع لازم برای محاسبات و ذخیره سازی داده‌ها کمتر خواهد بود و دوما اینکه اگر از روش خوبی برای کاهش ابعاد استفاده شده باشد همانطور که گفته شد اطلاعات غیرضروری در داده‌های کاهش بعد داده شده نخواهند بود و وقتی مدل برا آموزش داده‌های با کیفیت تری در اختیار داشته باشد طبیعتاً عملکرد بهتری خواهد داشت.\n",
        "<br>\n",
        "<br>\n",
        "بعد از کاهش ابعاد داده‌ها با استفاده از خوشه‌بندی می‌توان از آن‌ها برای آموزش یک مدل به طور با ناظر (با فرض موجود بودن برچسب داده‌ها) استفاده کرد.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMrjAR5DgjTZ"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    در مثال های زیر از دیتابیس ارقام دست نویس از دیتاست های موجود در sklearn.datasets استفاده شده است که در آن هر نمونه یک تصویر 8*8 از یک رقم دست نویس است.\n",
        "    <br>\n",
        "    لود کردن دیتابیس و نمایش یکی از نمونه های آن :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uX0JvBmMgjTZ",
        "outputId": "d143e0b5-ebd4-4518-df8a-84b882273d95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1797, 64)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.datasets import load_digits\n",
        "X_digits, y_digits = load_digits(return_X_y=True)\n",
        "\n",
        "X_digits.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8zP7PCKjgjTa",
        "outputId": "6dc971dc-a779-41e4-8049-46bd908b0d26"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "i=402\n",
        "X=X_digits[i].reshape(8,8)\n",
        "plt.imshow(X,cmap='gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e1z9WwKgjTa"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    همانطور که در بالا دیده شد هر نمونه یک تصویر 8*8 است یعنی هر نمونه 64 پیکسل یا ویژگی دارد. (در shape داده نیز این موضوع مشخص است)\n",
        "    <br>\n",
        "    <br>\n",
        "    حال با kmeans داده ها را با k=20 خوشه بندی میکنیم و به ازای هر نمونه به جای خود نمونه، فاصله اش از مراکز خوشه ها را جایگزین میکنیم.\n",
        "    <br>\n",
        "    در واقع هر نمونه ی 64 پیکسلی با یک آرایه 20 عنصری جایگزین میشود یعنی ابعاد ویژگی ها را از 64 به 20 کاهش داده ایم.\n",
        "    <br>\n",
        "    <br>\n",
        "    توجه: هرچند شاید به نظر برسد تعداد خوشه ها باید 10 باشد و 10 خوشه برای بیان داده ها کافی است، این موضوع را مد نظر داشته باشید که هر رقم به شکل های متفاوتی نوشته میشود پس ممکن است با 10 خوشه به نتیجه خوبی نرسیم."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1k7jH2IrgjTa",
        "outputId": "dfb970c1-0991-4aa9-d006-35531c71c8cf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1797, 20)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kmeans = KMeans(n_clusters=20).fit(X_digits)\n",
        "kmeans.transform(X_digits).shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGu8fzZlgjTa"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    بخشی از داده های کاهش بعد داده شده را به عنوان تست و بخش دیگر را به عنوان داده های آموزش جدا میکنیم و یک مدل logistic regression روی آن آموزش میدهیم.\n",
        "    <br>\n",
        "    میتوان دید علی رغم اینکه داده ها را کاهش بعد داده بودیم به دقت خوبی رسیدیم. کاهش بعد طبیعتا سرعت آموزش را بالا میبرد زیرا مدل پارامتر های کمتری خواهد داشت. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HJ2Yf3srgjTa",
        "outputId": "5710969c-ef39-4719-cda3-cef170c6dc37",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9755555555555555"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(kmeans.transform(X_digits), y_digits)\n",
        "log_reg = LogisticRegression(random_state=42)\n",
        "log_reg.fit(X_train, y_train)\n",
        "log_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh3SR4CkgjTb"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"3-3\">\n",
        "<font color=\"red\" size=4>**3-3. Semi-Supervised learning** </font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nbgJQjrgjTb"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "وقتی تعداد زیادی داده‌ی بدون برچسب داریم می‌توانیم از روش‌های یادگیری بدون ناظر استفاده کنیم. اما برای تسک‌هایی مانند  کلاس بندی نیاز داریم داده‌ها برچسب داشته باشند و از آنجا که نمی‌توانیم تمام داده‌ها را برچسب بزنیم (به دلیل هزینه بر بودن) مجبوریم تعداد کمی از آن‌ها را به نحوی انتخاب کنیم و فقط آن ها را برچسب زده و برای آموزش استفاده کنیم. می‌توان حدس زد اگر به طور کاملاً رندوم بخش نسبتا \n",
        "  کمی از داده‌ها را برچسب بزنیم و از آنها برای آموزش مدل استفاده کنیم نتیجه‌ی مطلوبی نخواهیم داشت.(در مثال زیر 50 نمونه از کل داده های ارقام دست نویس)\n",
        "    <br>\n",
        "    <br>\n",
        "    توجه : منظور از برچسب گذاری باید این باشد که ما پس از انتخاب نمونه ها به طور دستی تک به تک آن ها را ببینیم و برچسب آن ها را به طور دستی مشخص کنیم اما چون در  مثال ها در ادامه برچسب همه داده ها از قبل موجود است ما صرفا برچسب نظیر داده های انتخابی را به عنوان برچسبشان برای آموزش استفاده میکنیم. توجه کنید در یک مثال semi-supervised دنیای واقعی برچسب ها را در ابتدا نداریم و باید خودمان برچسب داده های انتخاب شده را مشخص کنیم."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1yd4yky3gjTb",
        "outputId": "436298a3-f3b6-40a8-c291-5bad158ed419"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7711111111111111"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.datasets import load_digits\n",
        "X_digits, y_digits = load_digits(return_X_y=True)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits)\n",
        "n_labeled = 50\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train[:n_labeled], y_train[:n_labeled])\n",
        "log_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rV_CjPcjgjTb"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "اینجا این ایده مطرح می‌شود که با استفاده از یک روش بدون ناظر (در اینجا خوشه‌بندی) از تمام داده‌ها استفاده کنیم و سپس از نتایج آن استفاده کنیم تا داده‌هایی را برای برچسب زدن انتخاب کنیم که شامل اطلاعات مفید تری باشند.\n",
        "<br>\n",
        "<br>\n",
        "یک روش این است که داده‌ها را خوشه‌بندی کنیم و سپس نزدیک‌ترین نمونه به مرکز هر خوشه را به عنوان نمیانده ی آن خوشه انتخاب کنیم. سپس نماینده‌های هر خوشه را به طور دستی برچسب گذاری کنیم و از آن‌ها به عنوان داده برای یادگیری با ناظر خود استفاده کنیم.\n",
        "<br>\n",
        "<br>\n",
        "    در مثال زیر با k=50 خوشه بندی را انجام داده و سپس نماینده ی هر خوشه را نمایش داده ایم.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLUXJcJAgjTc"
      },
      "outputs": [],
      "source": [
        "k = 50\n",
        "kmeans = KMeans(n_clusters=k)\n",
        "X_digits_dist = kmeans.fit_transform(X_train)\n",
        "representative_digit_idx = np.argmin(X_digits_dist, axis=0)\n",
        "X_representative_digits = X_train[representative_digit_idx]\n",
        "Y_representative_digits = y_train[representative_digit_idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ixOrpOfYgjTc",
        "outputId": "e9464c25-98c6-418c-d617-d713274bbbcf"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKpklEQVR4nO3d34tc9RnH8c+nq9JabRZaW2I2ZHIhASk0kRCQFNlELLGKVuhFAgoNhXijKC2I9kb6D4i9KMIStQFTpY26iFitoNEKrTWJm9ZkY0nDlmzQRinBnzREn17sCURZu2dmzq95fL8guDs77PcZzDvnzOzs+ToiBCCPr7Q9AIBqETWQDFEDyRA1kAxRA8mcV8c3tZ3yJfXly5c3ut6ll17a2Fpzc3ONrfXRRx81ttbHH3/c2FpNiwgvdnstUWd16623Nrrevffe29ha27dvb2ytmZmZlGt1BaffQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAypaK2vcX2m7aP2r677qEADG7JqG2PSfq1pGslXS5pm+3L6x4MwGDKHKk3SDoaEcci4rSkxyTdWO9YAAZVJuoVko6f8/l8cdtn2N5he5/tfVUNB6B/lf2WVkRMSZqS8v7qJTAKyhypT0haec7nE8VtADqoTNSvSbrM9mrbF0jaKumpescCMKglT78j4ozt2yQ9J2lM0kMRcaj2yQAMpNRz6oh4RtIzNc8CoAK8owxIhqiBZIgaSIaogWSIGkiGqIFkiBpIxnVsOt/ke797vV5TS2l6erqxtaRmd+i46aabGltrcnKysbWa/PvRtC/adocjNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyZTZoeMh2ydtv9HEQACGU+ZI/RtJW2qeA0BFlow6Il6W9J8GZgFQgcp26LC9Q9KOqr4fgMGw7Q6QDK9+A8kQNZBMmR9pPSrpz5LW2J63/dP6xwIwqDJ7aW1rYhAA1eD0G0iGqIFkiBpIhqiBZIgaSIaogWSIGkimsvd+t6XJbVXGx8cbW0uSXnrppcbW2rRpU2NrrVq1qrG1vow4UgPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kEyZa5SttP2i7cO2D9m+o4nBAAymzHu/z0j6eUQcsH2xpP22n4+IwzXPBmAAZbbdeSsiDhQfvy9pVtKKugcDMJi+fkvLdk/SOkmvLvI1tt0BOqB01LYvkvS4pDsj4r3Pf51td4BuKPXqt+3ztRD07oh4ot6RAAyjzKvflvSgpNmIuK/+kQAMo8yReqOkWyRttj1T/PlhzXMBGFCZbXdekeQGZgFQAd5RBiRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyI7+X1t69extba25urrG1pGYf2/T0dGNrHTx4sLG1vow4UgPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyZS58OBXbf/V9sFi251fNjEYgMGUeZvofyVtjogPiksFv2L7DxHxl5pnAzCAMhceDEkfFJ+eX/zhYv1AR5W9mP+Y7RlJJyU9HxGLbrtje5/tfRXPCKAPpaKOiE8iYq2kCUkbbH93kftMRcT6iFhf8YwA+tDXq98RcUrSi5K21DINgKGVefX7Etvjxcdfk3SNpCM1zwVgQGVe/V4uaZftMS38I/C7iHi63rEADKrMq99/08Ke1ABGAO8oA5IhaiAZogaSIWogGaIGkiFqIBmiBpIhaiCZkd92p0mTk5ONrrd27drG1pqZmWlsrSYfV6/Xa2wtqfmtmRbDkRpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWRKR11c0P9121x0EOiwfo7Ud0iarWsQANUou+3OhKTrJO2sdxwAwyp7pL5f0l2SPv2iO7CXFtANZXbouF7SyYjY///ux15aQDeUOVJvlHSD7TlJj0nabPuRWqcCMLAlo46IeyJiIiJ6krZKeiEibq59MgAD4efUQDJ9Xc4oIvZK2lvLJAAqwZEaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIZtdzqsya1wmjQ+Pt72CKlxpAaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIJlSbxMtriT6vqRPJJ3hMsBAd/Xz3u9NEfFubZMAqASn30AyZaMOSX+0vd/2jsXuwLY7QDeUPf3+fkScsP1tSc/bPhIRL597h4iYkjQlSbaj4jkBlFTqSB0RJ4r/npT0pKQNdQ4FYHBlNsj7uu2Lz34s6QeS3qh7MACDKXP6/R1JT9o+e//fRsSztU4FYGBLRh0RxyR9r4FZAFSAH2kByRA1kAxRA8kQNZAMUQPJEDWQDFEDybDtTh8mJycbXa/JbXdOnTrV2Fq9Xq+xtZp8XF3BkRpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWRKRW173PYe20dsz9q+su7BAAym7Hu/fyXp2Yj4se0LJF1Y40wAhrBk1LaXSbpK0k8kKSJOSzpd71gABlXm9Hu1pHckPWz7dds7i+t/fwbb7gDdUCbq8yRdIemBiFgn6UNJd3/+ThExFRHr2eYWaFeZqOclzUfEq8Xne7QQOYAOWjLqiHhb0nHba4qbrpZ0uNapAAys7Kvft0vaXbzyfUzS9vpGAjCMUlFHxIwknisDI4B3lAHJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDHtp9aHJPaAkaXp6urG1li1b1thau3btamwt9tICMPKIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkloza9hrbM+f8ec/2nQ3MBmAAS75NNCLelLRWkmyPSToh6cl6xwIwqH5Pv6+W9M+I+FcdwwAYXr+/0LFV0qOLfcH2Dkk7hp4IwFBKH6mLa37fIOn3i32dbXeAbujn9PtaSQci4t91DQNgeP1EvU1fcOoNoDtKRV1sXXuNpCfqHQfAsMpuu/OhpG/WPAuACvCOMiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaScURU/03tdyT1++uZ35L0buXDdEPWx8bjas+qiLhksS/UEvUgbO/L+hteWR8bj6ubOP0GkiFqIJkuRT3V9gA1yvrYeFwd1Jnn1ACq0aUjNYAKEDWQTCeitr3F9pu2j9q+u+15qmB7pe0XbR+2fcj2HW3PVCXbY7Zft/1027NUyfa47T22j9ietX1l2zP1q/Xn1MUGAf/QwuWS5iW9JmlbRBxudbAh2V4uaXlEHLB9saT9kn406o/rLNs/k7Re0jci4vq256mK7V2S/hQRO4sr6F4YEadaHqsvXThSb5B0NCKORcRpSY9JurHlmYYWEW9FxIHi4/clzUpa0e5U1bA9Iek6STvbnqVKtpdJukrSg5IUEadHLWipG1GvkHT8nM/nleQv/1m2e5LWSXq15VGqcr+kuyR92vIcVVst6R1JDxdPLXYWF90cKV2IOjXbF0l6XNKdEfFe2/MMy/b1kk5GxP62Z6nBeZKukPRARKyT9KGkkXuNpwtRn5C08pzPJ4rbRp7t87UQ9O6IyHJ55Y2SbrA9p4WnSpttP9LuSJWZlzQfEWfPqPZoIfKR0oWoX5N0me3VxQsTWyU91fJMQ7NtLTw3m42I+9qepyoRcU9ETERETwv/r16IiJtbHqsSEfG2pOO21xQ3XS1p5F7Y7HeDvMpFxBnbt0l6TtKYpIci4lDLY1Vho6RbJP3d9kxx2y8i4pn2RkIJt0vaXRxgjkna3vI8fWv9R1oAqtWF028AFSJqIBmiBpIhaiAZogaSIWogGaIGkvkfqVeEBw7xjLAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKn0lEQVR4nO3d34tc9RnH8c+nq9JabQKNKZIN3VxIQApNJAQkRUzEEquYXvQiEYWVQq4UlxZEe5PkH5DkoghL1BVMlTZqFLFaQYMVWmsS19ZkY0nDlmzQJlKCv6Ah+vRiT0qUtXtm5vzaJ+8XBHdnh3yfQd+eM7Mz5+uIEIA8vtH2AACqRdRAMkQNJEPUQDJEDSRzSR1/qW1eUq/A0qVLG1tryZIlja312WefNbbW9PR0Y2s1LSI81+21RI1q3HHHHY2tNTo62thak5OTja3V5OPqCk6/gWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkSkVte6Pt92wfs/1A3UMB6N+8UdsekvRrSbdIulbSFtvX1j0YgP6UOVKvlXQsIo5HxFlJT0naVO9YAPpVJuplkk5c8P1McduX2N5q+4DtA1UNB6B3lX1KKyLGJY1LfPQSaFOZI/VJScsv+H64uA1AB5WJ+i1J19heYfsySZslPV/vWAD6Ne/pd0Scs32PpJclDUl6NCIO1z4ZgL6Uek4dES9KerHmWQBUgHeUAckQNZAMUQPJEDWQDFEDyRA1kAxRA8mwQ0cPJiYmGl1v1apVja21bdu2xtbasWNHY2tdjDhSA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQTJkdOh61fcr2u00MBGAwZY7UE5I21jwHgIrMG3VEvC7p3w3MAqAClX1Ky/ZWSVur+vsA9Idtd4BkePUbSIaogWTK/ErrSUl/krTS9oztn9c/FoB+ldlLa0sTgwCoBqffQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDILftudsbGxxtYaGRlpbC2p2W13tm/f3tha+/bta2ytixFHaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkilzjbLltl+zfcT2Ydv3NTEYgP6Uee/3OUm/jIhDtq+UdND2KxFxpObZAPShzLY770fEoeLrjyVNSVpW92AA+tPTp7Rsj0haLenNOX7GtjtAB5SO2vYVkp6WNBYRH33152y7A3RDqVe/bV+q2aD3RMQz9Y4EYBBlXv22pEckTUXEQ/WPBGAQZY7U6yTdJWmD7cniz09qngtAn8psu/OGJDcwC4AK8I4yIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpJxRPWfvWjyAx2jo6NNLdW4ycnJxtbav39/Y2s1uSfZmTNnGluraREx55vCOFIDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8mUufDgN23/xfY7xbY7O5oYDEB/ylz3+z+SNkTEJ8Wlgt+w/fuI+HPNswHoQ5kLD4akT4pvLy3+cLF+oKPKXsx/yPakpFOSXomIObfdsX3A9oGKZwTQg1JRR8TnEbFK0rCktbZ/MMd9xiNiTUSsqXhGAD3o6dXviDgj6TVJG2uZBsDAyrz6fZXtxcXX35J0s6SjNc8FoE9lXv2+WtLjtoc0+z+B30bEC/WOBaBfZV79/qtm96QGsADwjjIgGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGklnw2+5k1uT2NPv27WtsrSa3+BkbG2tsraax7Q5wkSBqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiCZ0lEXF/R/2zYXHQQ6rJcj9X2SpuoaBEA1ym67MyzpVkm76x0HwKDKHql3Srpf0hdfdwf20gK6ocwOHbdJOhURB//f/dhLC+iGMkfqdZJutz0t6SlJG2w/UetUAPo2b9QR8WBEDEfEiKTNkl6NiDtrnwxAX/g9NZBMmQ3y/ici9kvaX8skACrBkRpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhm130LisW/xI0s6dOxtbi213gIsEUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyZS6nFFxJdGPJX0u6RyXAQa6q5drlK2PiA9rmwRAJTj9BpIpG3VI+oPtg7a3znUHtt0BuqHs6fePIuKk7aWSXrF9NCJev/AOETEuaVzio5dAm0odqSPiZPHPU5KelbS2zqEA9K/MBnnftn3l+a8l/VjSu3UPBqA/ZU6/vyfpWdvn7/+biHip1qkA9G3eqCPiuKQfNjALgArwKy0gGaIGkiFqIBmiBpIhaiAZogaSIWogGbbd6cGmTZsaXW/9+vWNrbV9+/bG1lq8eHFja01MTDS2liTdeOONja3FtjvARYKogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkSkVte7HtvbaP2p6yfX3dgwHoT9nrfu+S9FJE/Mz2ZZIur3EmAAOYN2rbiyTdIGlUkiLirKSz9Y4FoF9lTr9XSDot6THbb9veXVz/+0vYdgfohjJRXyLpOkkPR8RqSZ9KeuCrd4qI8YhYwza3QLvKRD0jaSYi3iy+36vZyAF00LxRR8QHkk7YXlncdJOkI7VOBaBvZV/9vlfSnuKV7+OS7q5vJACDKBV1RExK4rkysADwjjIgGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkin7jjJIeu655xpdr8m9tKanpxtba9GiRY2ttWvXrsbW6gqO1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMvNGbXul7ckL/nxke6yB2QD0Yd63iUbEe5JWSZLtIUknJT1b71gA+tXr6fdNkv4REf+sYxgAg+v1Ax2bJT051w9sb5W0deCJAAyk9JG6uOb37ZJ+N9fP2XYH6IZeTr9vkXQoIv5V1zAABtdL1Fv0NafeALqjVNTF1rU3S3qm3nEADKrstjufSvpuzbMAqADvKAOSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGUdE9X+pfVpSrx/PXCLpw8qH6Yasj43H1Z7vR8RVc/2glqj7YftA1k94ZX1sPK5u4vQbSIaogWS6FPV42wPUKOtj43F1UGeeUwOoRpeO1AAqQNRAMp2I2vZG2+/ZPmb7gbbnqYLt5bZfs33E9mHb97U9U5VsD9l+2/YLbc9SJduLbe+1fdT2lO3r256pV60/py42CPi7Zi+XNCPpLUlbIuJIq4MNyPbVkq6OiEO2r5R0UNJPF/rjOs/2LyStkfSdiLit7XmqYvtxSX+MiN3FFXQvj4gzLY/Vky4cqddKOhYRxyPirKSnJG1qeaaBRcT7EXGo+PpjSVOSlrU7VTVsD0u6VdLutmepku1Fkm6Q9IgkRcTZhRa01I2ol0k6ccH3M0ryH/95tkckrZb0ZsujVGWnpPslfdHyHFVbIem0pMeKpxa7i4tuLihdiDo121dIelrSWER81PY8g7J9m6RTEXGw7VlqcImk6yQ9HBGrJX0qacG9xtOFqE9KWn7B98PFbQue7Us1G/SeiMhyeeV1km63Pa3Zp0obbD/R7kiVmZE0ExHnz6j2ajbyBaULUb8l6RrbK4oXJjZLer7lmQZm25p9bjYVEQ+1PU9VIuLBiBiOiBHN/rt6NSLubHmsSkTEB5JO2F5Z3HSTpAX3wmavG+RVLiLO2b5H0suShiQ9GhGHWx6rCusk3SXpb7Yni9t+FREvtjcSSrhX0p7iAHNc0t0tz9Oz1n+lBaBaXTj9BlAhogaSIWogGaIGkiFqIBmiBpIhaiCZ/wLj8JCOhuSnjgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKkElEQVR4nO3d34tc9RnH8c+nq9JYrYHWFsmGbC5kIRTqSghIiqYRS6yie9GLBJRUCrlSDC2I9ir5B8ReFGGJGsHU0EYTRaxW8FeF1maTTFuzG0saLNmgjVLEH0hD9OnFnkC0a/fMzPm1T98vCO7ODvk+g749M7NnztcRIQB5fKXtAQBUi6iBZIgaSIaogWSIGkjmgjr+Utu8pV6BZcuWNbbWmjVrGltrZmamsbU++eSTxtZqWkR4odtriRrVGB8fb2yt6enpxtaamJhobK1er9fYWl3B028gGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIJlSUdveZPtN28dt31v3UAAGt2jUtkck/VLSjZLWSNpiu7kThQH0pcyRep2k4xFxIiLOSNor6dZ6xwIwqDJRr5B08rzv54rbPsf2NtvTtpv7ZACA/1LZp7QiYkrSlMRHL4E2lTlSn5K08rzvR4vbAHRQmagPSrrS9mrbF0naLOnpescCMKhFn35HxFnbd0p6XtKIpIcj4mjtkwEYSKnX1BHxrKRna54FQAU4owxIhqiBZIgaSIaogWSIGkiGqIFkiBpIhh06Omz79u1tj1CLHTt2NLbW5ORkY2t1BUdqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSKbNDx8O2T9t+o4mBAAynzJF6t6RNNc8BoCKLRh0Rr0r6VwOzAKhAZZ/Ssr1N0raq/j4Ag2HbHSAZ3v0GkiFqIJkyv9J6XNIfJI3bnrP9k/rHAjCoMntpbWliEADV4Ok3kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAzb7vRhw4YNja63devWRtdrSq/Xa3uE1DhSA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQTJlrlK20/ZLtGdtHbd/dxGAABlPm3O+zkn4WEYdtXyrpkO0XImKm5tkADKDMtjtvR8Th4usPJc1KWlH3YAAG09entGyPSZqQ9PoCP2PbHaADSkdt+xJJT0jaHhEffPHnbLsDdEOpd79tX6j5oPdExJP1jgRgGGXe/bakhyTNRsT99Y8EYBhljtTrJd0uaaPtXvHnhzXPBWBAZbbdeU2SG5gFQAU4owxIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZJb8XlpjY2ONrbV79+7G1pKkV155pbG1rrvuusbWOnLkSGNr/T/iSA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJFPmwoNftf0n238utt3Z2cRgAAZT5jTRf0vaGBEfFZcKfs32byPijzXPBmAAZS48GJI+Kr69sPjDxfqBjip7Mf8R2z1JpyW9EBELbrtje9r2dMUzAuhDqagj4tOIuErSqKR1tr+zwH2mImJtRKyteEYAfejr3e+IeF/SS5I21TINgKGVeff7ctvLi6+XSbpB0rGa5wIwoDLvfl8h6VHbI5r/n8CvI+KZescCMKgy737/RfN7UgNYAjijDEiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFklvy2Ozt27GhsrVWrVjW2VmYHDhxobK2nnnqqsbUkaXJystH1FsKRGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZEpHXVzQ/4htLjoIdFg/R+q7Jc3WNQiAapTddmdU0k2SdtU7DoBhlT1SPyDpHkmffdkd2EsL6IYyO3TcLOl0RBz6X/djLy2gG8ocqddLusX2W5L2Stpo+7FapwIwsEWjjoj7ImI0IsYkbZb0YkTcVvtkAAbC76mBZPq6nFFEvCzp5VomAVAJjtRAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkt+2539+/c3ttbY2Fhja0nS8uXLG1uryS2Fdu7c2dhaTW7x0xUcqYFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSKbUaaLFlUQ/lPSppLNcBhjorn7O/f5+RLxX2yQAKsHTbyCZslGHpN/ZPmR720J3YNsdoBvKPv3+XkScsv0tSS/YPhYRr55/h4iYkjQlSbaj4jkBlFTqSB0Rp4p/npa0X9K6OocCMLgyG+R9zfal576W9ANJb9Q9GIDBlHn6/W1J+22fu/+vIuK5WqcCMLBFo46IE5K+28AsACrAr7SAZIgaSIaogWSIGkiGqIFkiBpIhqiBZBxR/WnanPtdjSa3+en1eo2ttWHDhsbWavJxNS0ivNDtHKmBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkimVNS2l9veZ/uY7Vnb19Q9GIDBlL3u9y8kPRcRP7J9kaSLa5wJwBAWjdr2ZZKulfRjSYqIM5LO1DsWgEGVefq9WtK7kh6xfcT2ruL635/DtjtAN5SJ+gJJV0t6MCImJH0s6d4v3ikipiJiLdvcAu0qE/WcpLmIeL34fp/mIwfQQYtGHRHvSDppe7y46XpJM7VOBWBgZd/9vkvSnuKd7xOS7qhvJADDKBV1RPQk8VoZWAI4owxIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZMqeUYYWZN1LK/P+Vl3AkRpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSGbRqG2P2+6d9+cD29sbmA3AABY9TTQi3pR0lSTZHpF0StL+escCMKh+n35fL+nvEfGPOoYBMLx+P9CxWdLjC/3A9jZJ24aeCMBQSh+pi2t+3yLpNwv9nG13gG7o5+n3jZIOR8Q/6xoGwPD6iXqLvuSpN4DuKBV1sXXtDZKerHccAMMqu+3Ox5K+UfMsACrAGWVAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJOOIqP4vtd+V1O/HM78p6b3Kh+mGrI+Nx9WeVRFx+UI/qCXqQdiezvoJr6yPjcfVTTz9BpIhaiCZLkU91fYANcr62HhcHdSZ19QAqtGlIzWAChA1kEwnora9yfabto/bvrfteapge6Xtl2zP2D5q++62Z6qS7RHbR2w/0/YsVbK93PY+28dsz9q+pu2Z+tX6a+pig4C/af5ySXOSDkraEhEzrQ42JNtXSLoiIg7bvlTSIUmTS/1xnWP7p5LWSvp6RNzc9jxVsf2opN9HxK7iCroXR8T7LY/Vly4cqddJOh4RJyLijKS9km5teaahRcTbEXG4+PpDSbOSVrQ7VTVsj0q6SdKutmepku3LJF0r6SFJiogzSy1oqRtRr5B08rzv55TkP/5zbI9JmpD0esujVOUBSfdI+qzlOaq2WtK7kh4pXlrsKi66uaR0IerUbF8i6QlJ2yPig7bnGZbtmyWdjohDbc9SgwskXS3pwYiYkPSxpCX3Hk8Xoj4laeV5348Wty15ti/UfNB7IiLL5ZXXS7rF9luaf6m00fZj7Y5UmTlJcxFx7hnVPs1HvqR0IeqDkq60vbp4Y2KzpKdbnmlotq3512azEXF/2/NUJSLui4jRiBjT/L+rFyPitpbHqkREvCPppO3x4qbrJS25Nzb73SCvchFx1vadkp6XNCLp4Yg42vJYVVgv6XZJf7XdK277eUQ8295IKOEuSXuKA8wJSXe0PE/fWv+VFoBqdeHpN4AKETWQDFEDyRA1kAxRA8kQNZAMUQPJ/AczRHaf2Xvu+QAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKwklEQVR4nO3d34tc9RnH8c+nq9JYbQKtLZIN3VzIQigkKyEgKZJELLGK5qIXCShsKORKSWhBtHf9ByS9KMISTQVTpY1KRKxW0GCF1rpJtq3JJiXNKtmgjVKixouG6NOLPYFoN90zM+fXPr5fENyZHfb7DOadMzM7c76OCAHI42ttDwCgWkQNJEPUQDJEDSRD1EAyV9XxQ22nfEl9yZIlja63atWqxtY6ceJEY2udP3++sbUyiwjPd30tUWc1Ojra6HqTk5ONrbVx48bG1jp48GBja30V8fAbSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkimVNS2N9s+Yfuk7YfqHgpA/xaM2vaQpF9JukPSKknbbDf3pmQAPSlzpF4n6WREnIqIC5KelnRPvWMB6FeZqJdLOn3Z5dniui+wvcP2pO3mPoUA4H9U9imtiJiQNCHl/eglsBiUOVKfkbTissvDxXUAOqhM1G9Jusn2StvXSNoq6fl6xwLQrwUffkfERdv3S3pZ0pCkxyPiaO2TAehLqefUEfGipBdrngVABXhHGZAMUQPJEDWQDFEDyRA1kAxRA8kQNZCM69h0Put7v5veWWLZsmWNrbVly5bG1lq9enVjax04cKCxtZp2pW13OFIDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZBMmR06Hrd91vbbTQwEYDBljtS/lrS55jkAVGTBqCPidUn/bmAWABWobIcO2zsk7ajq5wHoD9vuAMnw6jeQDFEDyZT5ldZTkv4kadT2rO2f1D8WgH6V2UtrWxODAKgGD7+BZIgaSIaogWSIGkiGqIFkiBpIhqiBZBb9tjsjIyNNLaWZmZnG1pKk7du3N7bWhg0bGluryS1+mvz7IUnnzp1rbC223QG+IogaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkimzDnKVth+zfYx20dt72xiMAD9KXPe74uSfhYRh21fL+mQ7Vci4ljNswHoQ5ltd96LiMPF159Impa0vO7BAPSnpx06bI9IGpP05jzfY9sdoANKR237OknPSNoVER9/+ftsuwN0Q6lXv21frbmg90XEs/WOBGAQZV79tqTHJE1HxCP1jwRgEGWO1Osl3Sdpk+2p4s+Pap4LQJ/KbLvzhqR5T5sCoHt4RxmQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyfT0Ka0u2rVrV2NrffTRR42tJUlTU1ONrbV3797G1jpw4EBja42Pjze2liTt3r270fXmw5EaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkimzIkHv277L7b/Wmy784smBgPQnzJvE/2PpE0Rcb44VfAbtn8fEX+ueTYAfShz4sGQdL64eHXxh5P1Ax1V9mT+Q7anJJ2V9EpEzLvtju1J25MVzwigB6WijojPImKNpGFJ62x/f57bTETE2ohYW/GMAHrQ06vfEXFO0muSNtcyDYCBlXn1+wbby4qvl0i6XdLxmucC0Kcyr37fKOkJ20Oa+0fgtxHxQr1jAehXmVe//6a5PakBLAK8owxIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZBb9tjtr1qxpbK2lS5c2tpYkHTlypLG1mt5SqCkzMzNtj9A4jtRAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRTOurihP5HbHPSQaDDejlS75Q0XdcgAKpRdtudYUl3StpT7zgABlX2SL1b0oOSPr/SDdhLC+iGMjt03CXpbEQc+n+3Yy8toBvKHKnXS7rb9juSnpa0yfaTtU4FoG8LRh0RD0fEcESMSNoq6dWIuLf2yQD0hd9TA8n0dDqjiDgo6WAtkwCoBEdqIBmiBpIhaiAZogaSIWogGaIGkiFqIBlHRPU/1K7+h15Bk9vujI+PN7aWJO3cubOxtcbGxhpba2pqqrG1MosIz3c9R2ogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIpdTqj4kyin0j6TNJFTgMMdFcv5yjbGBEf1jYJgErw8BtIpmzUIekPtg/Z3jHfDdh2B+iGsg+/fxARZ2x/R9Irto9HxOuX3yAiJiRNSM1+9BLAF5U6UkfEmeK/ZyU9J2ldnUMB6F+ZDfK+Yfv6S19L+qGkt+seDEB/yjz8/q6k52xfuv1vIuKlWqcC0LcFo46IU5JWNzALgArwKy0gGaIGkiFqIBmiBpIhaiAZogaSIWogmUW/7U6TRkZGGl1vZmamsbXYdmfxYdsd4CuCqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZEpFbXuZ7f22j9uetn1L3YMB6E/Z837/UtJLEfFj29dIurbGmQAMYMGobS+VdKukcUmKiAuSLtQ7FoB+lXn4vVLSB5L22j5ie09x/u8vYNsdoBvKRH2VpJslPRoRY5I+lfTQl28UERMRsZZtboF2lYl6VtJsRLxZXN6vucgBdNCCUUfE+5JO2x4trrpN0rFapwLQt7Kvfj8gaV/xyvcpSdvrGwnAIEpFHRFTkniuDCwCvKMMSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWTKvqMMkjZs2NDoeu+++25ja7G/VR4cqYFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZBaM2vao7anL/nxse1cDswHow4JvE42IE5LWSJLtIUlnJD1X71gA+tXrw+/bJP0zIpp7UzKAnvT6gY6tkp6a7xu2d0jaMfBEAAZS+khdnPP7bkm/m+/7bLsDdEMvD7/vkHQ4Iv5V1zAABtdL1Nt0hYfeALqjVNTF1rW3S3q23nEADKrstjufSvpWzbMAqADvKAOSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGUdE9T/U/kBSrx/P/LakDysfphuy3jfuV3u+FxE3zPeNWqLuh+3JrJ/wynrfuF/dxMNvIBmiBpLpUtQTbQ9Qo6z3jfvVQZ15Tg2gGl06UgOoAFEDyXQiatubbZ+wfdL2Q23PUwXbK2y/ZvuY7aO2d7Y9U5VsD9k+YvuFtmepku1ltvfbPm572vYtbc/Uq9afUxcbBPxDc6dLmpX0lqRtEXGs1cEGZPtGSTdGxGHb10s6JGnLYr9fl9j+qaS1kr4ZEXe1PU9VbD8h6Y8Rsac4g+61EXGu5bF60oUj9TpJJyPiVERckPS0pHtanmlgEfFeRBwuvv5E0rSk5e1OVQ3bw5LulLSn7VmqZHuppFslPSZJEXFhsQUtdSPq5ZJOX3Z5Vkn+8l9ie0TSmKQ3Wx6lKrslPSjp85bnqNpKSR9I2ls8tdhTnHRzUelC1KnZvk7SM5J2RcTHbc8zKNt3STobEYfanqUGV0m6WdKjETEm6VNJi+41ni5EfUbSissuDxfXLXq2r9Zc0PsiIsvplddLutv2O5p7qrTJ9pPtjlSZWUmzEXHpEdV+zUW+qHQh6rck3WR7ZfHCxFZJz7c808BsW3PPzaYj4pG256lKRDwcEcMRMaK5/1evRsS9LY9ViYh4X9Jp26PFVbdJWnQvbPa6QV7lIuKi7fslvSxpSNLjEXG05bGqsF7SfZL+bnuquO7nEfFieyOhhAck7SsOMKckbW95np61/istANXqwsNvABUiaiAZogaSIWogGaIGkiFqIBmiBpL5L2zomdZ0tp8UAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(50):\n",
        "    X=X_representative_digits[i].reshape(8,8)\n",
        "    plt.imshow(X,cmap='gray')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jyjnRD7gjTc",
        "outputId": "8eededa2-5d39-47ba-d8e3-8a9d1db0af30"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 3, 4, 7, 9, 6, 1, 5, 9, 7, 2, 0, 9, 3, 2, 4, 9, 2, 1, 4, 7,\n",
              "       7, 3, 5, 5, 1, 4, 8, 0, 8, 4, 5, 4, 3, 9, 5, 6, 6, 5, 2, 9, 0, 1,\n",
              "       7, 8, 4, 4, 9, 8])"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_representative_digits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFnVarLhgjTc",
        "outputId": "5949aa97-040b-4b19-a7e7-52eda8929b72"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8844444444444445"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_representative_digits, Y_representative_digits)\n",
        "log_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2dtYugngjTd"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "    میتوان دید استفاده از خوشه بندی برای انتخاب داده هایی که برچسب میزنیم، باعث افزایش دقت شد.\n",
        "    <br>\n",
        "    <br>\n",
        "اگر کمی این ایده را بسط دهیم به روش label propagation   یا انتشار برچسب می‌رسیم. در این ایده بعد از برچسب زدن نماینده‌ی هر خوشه، به کل اعضای آن خوشه نیز برچسب نماینده اش را نسبت می‌دهیم و درواقع برچسبش را به کل خوشه منتشر می‌کنیم و از کل داده‌ها برای آموزش به روش با ناظر خود استفاده خواهیم کرد."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fk2biiJQgjTd",
        "outputId": "eec16e3e-4bf6-42e2-efbd-fff2c00bac0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9133333333333333"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_propagated = np.empty(len(X_train), dtype=np.int32)\n",
        "for i in range(k):\n",
        "    y_train_propagated[kmeans.labels_==i] = Y_representative_digits[i]\n",
        "    \n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train_propagated)\n",
        "log_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMWMqB69gjTd"
      },
      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "        میتوان دید استفاده از label propagation، باعث افزایش دقت شد.\n",
        "    <br>\n",
        "    <br>\n",
        "اشکال روش قبل در این است که توجهی به فاصله‌ی نمونه‌ها با مرکز خوشه ندارد. نمونه‌هایی که به مرکز خوشه و در نتیجه نماینده‌ی خوشه نزدیک هستند به احتمال بالایی همان برچسب نماینده خود را خواهند داشت؛ اما در مورد نمونه‌های نزدیک به مرز خوشه‌ها نمی‌توان با همین اطمینان اظهار نظر کرد. به همین منظور در یک بهبود برای روش قبل، برچسب نماینده را تنها به درصد نزدیکی از نمونه‌ها به مرکز انتشار می‌دهیم. مثلاً فقط به 50 درصد نزدیک‌ترین نمونه‌ها به مرکز هر خوشه برچسب نمیانده ی آن خوشه را نسبت می‌دهیم و سپس از تمام داده‌های برچسب گذاری شده برای آموزش استفاده میکینم. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WIwNC1m9gjTd",
        "outputId": "1c7fdc81-0e79-40b0-fdae-651f4b071b0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9155555555555556"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "percentile_closest = 50\n",
        "X_cluster_dist = X_digits_dist[np.arange(len(X_train)), kmeans.labels_]\n",
        "for i in range(k):\n",
        "    in_cluster = (kmeans.labels_ == i)\n",
        "    cluster_dist = X_cluster_dist[in_cluster]\n",
        "    cutoff_distance = np.percentile(cluster_dist, percentile_closest)\n",
        "    above_cutoff = (X_cluster_dist > cutoff_distance)\n",
        "    X_cluster_dist[in_cluster & above_cutoff] = -1\n",
        "partially_propagated = (X_cluster_dist != -1)\n",
        "X_train_partially_propagated = X_train[partially_propagated]\n",
        "y_train_partially_propagated = y_train_propagated[partially_propagated]\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train_partially_propagated, y_train_partially_propagated)\n",
        "log_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
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        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "        میتوان دید انتشار برچسب تنها به نمونه های نزدیک تر به مرکز خوشه که اطمینان بالایی دارند، اندکی دقت را بهبود داد."
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      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"3-4\">\n",
        "<font color=\"red\" size=4>**3-4.یادگیری فعال (Active learning):** </font>"
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      "source": [
        "<font face=\"XB Zar\" size=4><div dir=rtl>\n",
        "در این رویکرد ناظر با الگوریتم یادگیری ارتباط متفابل دارد به این شکل که هر زمان الگوریتم برای داده‌هایی که اعلام می‌کند نیاز به برچسب داشت، ناظر آن داده‌ها را برایش برچسب گذاری کند.\n",
        "<br>\n",
        "<br>\n",
        "یه روش یادگیری فعال uncertainty sampling  نام دارد به این صورت که هر بارا لگوریتم با داده‌های برچسب داری که دراختیار دارد آموزش را انجام می‌دهد و روی داده‌های بدون برچسب پیش بینی انجام می‌دهد. با ازای هر پیش بینی یه ضریب اطمینان نیز محاسبه می‌کند و داده‌هایی را که به پیش بینی شان کمترین اطمینان را دارد به ناظر می‌دهد تا برایش برچسب بزند و این مراحل را تکرار میکتد تا زمانی که بهبود قابل توجهی حاصل نشود.\n",
        "<br>\n",
        "<br>\n",
        "روش‌های دیگر یادیگری فعال می‌تواند شامل موارد زیر باشد:\n",
        "<br>\n",
        "-\tالگوریتم داده‌هایی که موجب بیشترین کاهش در خطا باشد را برای برچسب زدن به ناظر بدهد\n",
        "-\tچند الگوریتم روی داده‌ها اجرا شود و نمونه‌هایی که الگوریتم‌های مختلف پیش بینی‌های مختلفی برایشان داشته است برای برچسب زدن به ناظر داده شوند.\n",
        "\n",
        "\n"
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      "source": [
        "<hr>\n",
        "<font face=\"XB Zar\" size=4><div dir=rtl id=\"2\">\n",
        "<font color=\"black\" size=5> منابع</font>\n"
      ]
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      },
      "source": [
        "\n",
        "- GeÌron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (2nd ed.). O’Reilly.\n",
        "\n",
        "- https://github.com/asharifiz/Introduction_to_Machine_Learning/blob/main/Slides/Chapter_02_Classical_Models/Clustering/section%202-3.pdf"
      ]
    }
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